Crewship – Deploy AI agents to production in one command

Hacker News

Hey HN! We built Crewship ( https://crewship.dev ) because deploying AI agents to production is still unnecessarily painful. If you've built something with CrewAI, LangGraph, or similar frameworks, you know the drill: it works great locally, then you spend days figuring out infrastructure, scaling, monitoring, and artifact management just to get it running for real users. Crewship handles all of that. You add a crewship.toml to your project, run `crewship deploy`, and your agents are live in seconds. It's framework-agnostic — we currently support CrewAI, LangGraph, and LangGraph.js, with more coming. What you get: - One-command deploy (no Docker/K8s config needed) - Real-time streaming of agent actions via SSE - Automatic artifact collection (every file/report your agents produce) - Auto-scaling from zero to thousands of concurrent runs - Version control with instant rollback - Secrets management We're a small team in Switzerland, and we've been using this ourselves for months. Free tier available — would love your feedback. Docs: https://docs.crewship.dev Quickstart: https://docs.crewship.dev/quickstart

Visit product View on Hacker NewsFeb 27, 2026

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, user · Missing: mac, macos, cursor

95%95% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released

56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: month, users · Missing: mobile apps, ios, personal

37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

29%29% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: users · Missing: plus, platform, intuitive

19%19% predicted probability of success on AppSumo, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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